From Errors to Solutions: LLM-Powered Command Scripting for FPGA Cad Tools
Bibliographic record
Abstract
Computer-aided design (CAD) tools provide hundreds or even thousands of options that control various optimizations throughout the design flow. While this flexibility is powerful, it requires significant experience to be familiar with those options and effectively utilize them. For example, when a design fails, in many cases errors can be resolved by adjusting the CAD tool options rather than modifying the design itself. In this work, we propose VPR-LLM, a tool that utilizes Large Language Models (LLMs) to automate error resolution in the open-source FPGA CAD tool Verilog-to-Routing (VTR) by modifying the command-line options used to run the tool. VPRLLM parses error logs, VTR help messages, and documentation, then utilizes an LLM to generate modified command-line options that resolve the issue. VPR-LLM supports various LLM models and prompting techniques. All these models and techniques are evaluated and compared in terms of efficiency and cost. To evaluate our method, we proposed a dataset of 26 VTR run failures spanning five distinct error categories. The proposed technique successfully resolved 80 % of the cases without requiring any fine-tuning to the LLM model, demonstrating the effectiveness of VPR-LLM. This work represents an initial step toward AI-assisted debugging in CAD flows, where LLMs can enhance productivity by automatically identifying and correcting tool configurations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".